Microsoft Technology Licensing, LLC

Markus Cozowicz

John C Langford

Olga Ivanova

Siddhartha Sen

Alexandre De Baynast

Venkat Narayanan

Smita Ojha

Aleksandrs Slivkins

Alekh Agarwal

Yi Wang

Ulrich Pinsdorf

Philipp Kranen

The claimed subject matter includes techniques for providing an application testing service with a user interface that enables a user to evaluate performance data for computer implemented decision policies. An example method includes displaying a first reward statistic comprising an actual performance result for a policy implemented by an application. The method also includes obtaining experimental data corresponding to previously implemented policies, computing a second reward statistic for a hypothetical policy using a reward function applied to the experimental data. The method also includes displaying the second reward statistic together with the first reward statistic to enable a user to compare the first reward statistic and the second first reward statistic.

Application testing

Anomalies detection in error signals of a cloud based service is provided. An application such as an analysis application identifies a machine learning algorithm that matches error signals of components of a cloud based service. A periodic pattern from the error signals is removed with the machine learning algorithm to filter the periodic pattern from an error count in the error signals. The error signals are processed with the machine learning algorithm to detect one or more anomalies with the components. The machine learning algorithm is updated while processing new data to detect new patterns.

Detection of anomalies in error signals of cloud based service

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Markus Cozowicz